Ultimate Azure Data Factory: Cloud Data Engineering

Real world Modern Data Warehouse project for Data Engineers using Azure Data Factory, Sql, Data Lake, Databricks

What you'll learn

You will learn how to build data pipelines in Azure Data Factory (ADF) through a step-by-step approach.

You will learn how to ingest data in different formats into Azure Data Lake Gen2 using Azure Data Factory (ADF)

You will learn how to use and build various types of transformations in Azure Data Factory (ADF)

You will learn hands-on implementations of building generic artifacts in Azure Data Factory (ADF) such as Flowlets and Templates

You will learn how to transform data into the Medallion layers in Azure Data Lake Gen2 using Data Flows in Azure Data Factory (ADF)

You will learn how to implement ETL/ELT using Azure Data Factory (ADF) in order to implement a Data Warehouse

You will learn how to create generic metadata driven pipelines in Azure Data Factory (ADF) to implement the ETL/ELT processes

You will learn the concepts of the Modern Data Warehouse Architecture and the Delta Lake

You will learn the concepts of Slowly Changing Dimensions and how to implement them in Azure Data Factory (ADF)

You will learn how to load transformed data from Azure Data Lake Storage Gen2 to Azure SQL Database using Azure Data Factory (ADF)

You will learn how to implement a Delta Lake using Databricks Notebook Activity in Azure Data Factory (ADF) and load into Azure Data Lake Storage Gen2

You will learn how to transform your raw data into a finished data warehouse using Azure Data Factory (ADF) and then visualize it in PowerBI

You will learn how to build pipelines using good practices and naming standards as in a typical real-world data engineering project

You will learn how to implement different types of Triggers in Azure Data Factory (ADF) and how to schedule your data pipelines

You will learn how to monitor pipelines using Azure Data Factory (ADF), Azure Monitor, and how to recover from pipeline failures

By the end of this course you will have learnt all the topics required on Azure Data Factory to pass the Azure Data Engineer Associate Certification Exam DP203

Requirements

Basic understanding of Sql will be beneficial

Basic understanding of cloud computing will be beneficial

Experience in Azure is not required, we will learn this step by step within this course as we build the project

Understanding of data warehouses will be beneficial, but not necessary, we will learn it as we build the project in the course

An Azure account is required for the course, we will learn how you can create it during the course

Description

Welcome!Data engineering is a thriving focus in the IT industry, with Microsoft's Azure Data Factory emerging as a sought-after tool in cloud-based data engineering.Join this course for a step-by-step journey into mastering Azure Data Factory (ADF). Using a real-world scenario of an e-commerce company grappling with data integration and insights, we'll explore the data of an online wine retailer, showcasing how implementing a modern data warehouse with ADF can provide solutions.Distinguishing itself from other Udemy offerings on Azure Data Factory and Data Engineering Technologies, this course guides you hands-on in transforming raw data into a Modern Data Warehouse using Azure Data Factory (ADF). Upon completion, you'll gain proficiency in ADF, ready to tackle real-world data engineering projects.Given the course's focus on real-world business scenarios, it adopts a sequential approach mirroring how such requirements unfold in actual projects. This method ensures you not only implement business needs but also grasp the technical concepts explained at each stage of implementing data pipelines with Azure Data Factory (ADF).This course covers more than just modern data warehouse concepts like architecture, medallion layers, and delta lake. You'll also gain expertise in utilizing diverse Azure ecosystem solutions, including Azure Data Lake Storage, Azure SQL Database, and Azure Databricks. Additionally, you'll learn to visually represent the completed data warehouse through Power BI reports.This course enables you to grasp concepts and skills assessed in the Azure Data Engineer Associate Certification exam DP203. While it equips you with the necessary skills, it's important to note that the course is not designed solely for certification passing but for comprehensive learning.I appreciate your time, and I've crafted this course to be practical and focused. I aim for simplicity and conciseness, starting from the basics and ensuring proficiency in the technologies covered.Currently the course teaches you the following:Azure Data FactoryConstructing a contemporary Data Warehouse architecture for a data engineering solution involves utilizing Azure Data Engineering technologies like Azure Data Factory (ADF), Azure Data Lake Gen2, Azure SQL Database, Azure Databricks, Azure KeyVault, and Microsoft PowerBI.Incorporating data from varied sources with diverse formats into Azure Data Lake Gen2 is achieved through the use of Azure Data Factory.Comprehending Azure concepts, including resources and their provisioning methods.Learning to incorporate and use tools such as Azure Storage Explorer, Azure Data Studio, and Visual Studio Code in the development workflow.Implementing Azure Data Factory (ADF) pipelines using different control flow activities such as Get Metadata, ForEach, If Conditions, etc.Using Parameters and Variables in Pipelines, Datasets and LinkedServices to create generic parameter driven pipelines in Azure Data Factory (ADF).Using parameters in conjunction with Azure KeyVault to create generic parameter driven piplines in Azure Data Factory (ADF).Implementing Mapping Data Flows to create transformation logic to handle a variety of transformation scenarios such as Filter, Conditional Split, Derived Column, Aggregate, Join, Select, and Sink transformation.Developing universal components in data pipelines, such as Flowlets, and mastering the swift development of data processing needs through pre-built pipeline templates.Learning how to implement error handling in data pipelines and controlling pipeline flow.Implementig data quality rules using the Assert transformation within a data pipeline. Implementing data pipelines to handle common slowly changing dimension scenarios such as SCD Type 1 and SCD Type 2.Implementing data pipleines to implement a Fact table.Learning how to debug data pipelines and resolving issues.Implementing pipeline scheduling using different types of triggers such as Event Trigger, Schedule Trigger and Tumbling Window Trigger in Azure Data Factory (ADF)Implementing Azure Data Factory pipelines to invoke Mapping Data Flows and executing them.Creating ADF pipelines to execute Databricks Notebook activities to carry out transformations and implement a Delta Lake table.Creating pipeline dependencies and using the Pipeline activity to orchestrate the ETL/ELT process.Implementing trigger dependencies to understand how to chain pipelines and orchestrate the data flow.Monitoring data pipelines, creating alert notifications, and reporting data factory metrics using Azure Data Factory Monitor.Understanding how to monitor Azure Data Factory pipelines using Azure Monitor using specific Data Factory  metrics.Modern Data WarehouseUnderstand the different types of Data Warehouse Architectures.Understand the concepts of a Delta Lake.Understand the Dimensional Model and a Star Schema based Data Warehouse.Understand the concept of Medallion Layers and how to implement it within the Azure Data Lake Storage.Azure DatabricksUnderstand the creation of an Azure Databricks Workspace, Databricks clusters, Mounting storage accounts, Creating Databricks notebooks, performing transformations using Databricks notebooks, and Invoking Databricks notebooks from Azure Data Factory.Understand the implementation of a Delta Lake table using Azure Databricks Notebook activity from an Azure Data Factory pipeline.Understand the concepts of Optimizing a Delta Lake Table, Time Travel, Vacuuming, and Delta Logs.Azure Resources and Azure Storage SolutionsLearn the different approaches to creating Azure Resources.Learn how to create an Azure Storage Account resource, creating containers, and how to upload data through the Azure Portal or through Azure Storage Explorer into the Azure storage resource. Learn how to create an Azure SQL Database resource, understand the Pricing Tiers, Creating an Admin User, Creating Tables, Loading Data, Querying the database and interacting with Azure Sql Database through Azure Data Studio.

Overview

Section 1: Overview

Lecture 1 Welcome

Lecture 2 What you will learn?

Lecture 3 Goal of this course

Lecture 4 Commitment

Lecture 5 Course Materials

Lecture 6 Course Slides

Section 2: Introduction

Lecture 7 Introduction to Azure Data Factory

Lecture 8 Why Azure Data Factory?

Lecture 9 What is Azure Data Factory?

Lecture 10 Benefits of Azure Data Factory

Lecture 11 Azure Account

Lecture 12 User Interface Azure Portal

Lecture 13 Module Summary

Section 3: Project Overview

Lecture 14 Hands-On Project Overview

Lecture 15 Business Case for the Project

Lecture 16 Solution Requirements

Lecture 17 Architectural Patterns

Lecture 18 Modern Data Warehouse Architecture

Lecture 19 Hands-On Project Architecture

Lecture 20 Repositories

Lecture 21 Module Summary

Section 4: Environment

Lecture 22 Module Overview

Lecture 23 Software Tools

Lecture 24 Software Tools Setup

Lecture 25 Azure Resources

Lecture 26 Setup Azure Resources

Lecture 27 Setup Azure Resources in Azure Portal

Lecture 28 Setup Azure Resource Group

Lecture 29 Setup Azure Data Lake Storage

Lecture 30 Setup Azure Data Factory Resource

Lecture 31 Setup Azure Sql DB Resource

Lecture 32 Review Azure Resources

Lecture 33 Setup Azure Data Studio

Lecture 34 Setup Azure Storage Explorer

Lecture 35 Module Summary

Section 5: Building a Data Pipeline

Lecture 36 Module Overview

Lecture 37 Building Blocks of Azure Data Factory - Main Components

Lecture 38 Building Blocks of Azure Data Factory - Pipelines and Activities

Lecture 39 Building Blocks of Azure Data Factory - How they Tie Together

Lecture 40 Azure Data Factory User Interface - Main Page

Lecture 41 Azure Data Factory User Interface - Authoring Canvas

Lecture 42 Data Sources

Lecture 43 Data Sources - Data Ingestion

Lecture 44 Data Sources - Data Organization

Lecture 45 Building the Data Pipeline

Lecture 46 Building the Data Pipeline - Creating the Containers

Lecture 47 Building the Data Pipeline - Creating the Pipeline

Lecture 48 Building the Data Pipeline - Review and Organize

Lecture 49 Importing Semi-Structured Data

Lecture 50 Importing Semi-Structured Data - Building the Pipeline

Lecture 51 Importing Semi-Structured Data - Organizing the Pipeline

Lecture 52 Importing Semi-Structured Data - Recap of the Lesson

Lecture 53 Naming Conventions

Lecture 54 Module Summary

Section 6: Pipeline Activities and Parameters

Lecture 55 Module Overview

Lecture 56 Activities

Lecture 57 Activity Dependencies

Lecture 58 Activity Dependencies - Examples

Lecture 59 Copy Activity

Lecture 60 Copy Activity Concepts - Examples

Lecture 61 Expressions and Variables

Lecture 62 Expressions and Variables - Examples

Lecture 63 Parameters

Lecture 64 Parameters - Examples

Lecture 65 Azure Key Vault - Overview

Lecture 66 Azure Key Vault - Setup

Lecture 67 Azure Key Vault - Create Linked Service

Lecture 68 Importing Semi-Structured Data

Lecture 69 Module Summary

Section 7: Mapping Data Flows

Lecture 70 Module Overview

Lecture 71 Introduction to Mapping Data Flows

Lecture 72 Scenarios for Mapping Data Flows

Lecture 73 User Interface of Mapping Data Flows

Lecture 74 User Interface of Mapping Data Flows - Debug Feature

Lecture 75 Implementing a Mapping Data Flow - Overview

Lecture 76 Implementing a Mapping Data Flow - Pipeline and Data Sources

Lecture 77 Implementing a Mapping Data Flow - Adding Transformations

Lecture 78 Implementing a Mapping Data Flow - Pipeline Execution

Lecture 79 Mapping Data Flow - Concepts

Lecture 80 Mapping Data Flow - Concepts Example

Lecture 81 Performance of Mapping Data Flows - Integration Runtime

Lecture 82 Performance of Mapping Data Flows

Lecture 83 Module Summary

Section 8: Implementing Flowlets

Lecture 84 Module Overview

Lecture 85 Introduction to Flowlets

Lecture 86 Scenarios for Flowlets

Lecture 87 User Interface of Flowlets - Overview

Lecture 88 User Interface of Flowlets - Create a Demo Flowlet

Lecture 89 Implementing a Flowlet - Create Flowlet

Lecture 90 Implementing a Flowlet - Use the Flowlet

Lecture 91 Module Summary

Section 9: Controlling Pipeline Flow

Lecture 92 Module Overview

Lecture 93 Asserts

Lecture 94 Implementing Asserts - Assert Expect True

Lecture 95 Implementing Asserts - Identifying Error Rows

Lecture 96 Implementing Asserts - Processing Error Rows

Lecture 97 Error Handling Overview

Lecture 98 Implementing Error Handling - Fail Activity

Lecture 99 Implementing Error Handling - Capturing Errors

Lecture 100 Implementing Error Handling - Logging Errors

Lecture 101 Implementing Error Handling - Review of Error Pipeline

Lecture 102 Integrating Data Quality and Error Handling

Lecture 103 Building Pipelines using Pre-Built Templates

Lecture 104 Module Summary

Section 10: Building the Data Warehouse - Part 1

Lecture 105 Module Overview

Lecture 106 Data Warehouse Overview

Lecture 107 Data Warehouse Models

Lecture 108 Data Warehouse Vino World

Lecture 109 Data Process

Lecture 110 Building the Azure Sql Database - Create the Stage Tables

Lecture 111 Building the Azure Sql Database - Create the DW Tables

Lecture 112 Building the Staging Layer Master Data - Master data

Lecture 113 Building the Staging Layer Master Data - Product data

Lecture 114 Building the Staging Layer Master Data - Metadata approach

Lecture 115 Building the Staging Layer Master Data - Create Parameter Datasets

Lecture 116 Building the Staging Layer Master Data - Create Metadata Pipeline

Lecture 117 Building the Staging Layer Master Data - Pipeline execution

Lecture 118 Building the Staging Layer Transaction Data

Lecture 119 Building the Staging Layer Product Data - Combine Product Data

Lecture 120 Building the Staging Layer Transaction Data - Combine Sales Data

Lecture 121 Module Summary

Section 11: Building the Data Warehouse - Part 2

Lecture 122 Module Overview

Lecture 123 Dimensions - Overview of Dimensions

Lecture 124 Dimensions - Slowly Changing Dimensions

Lecture 125 Dimensions - Master Dimensions and SCD Type

Lecture 126 Building Type1 Dimensions - Using Data Flows

Lecture 127 Building Type 1 Dimensions - Pipeline Review

Lecture 128 Building Type 1 Dimensions - Using Stored Procedures

Lecture 129 Dimensions - Overview of Type2 Dimensions

Lecture 130 Building Type 2 Dimensions - Product Dimension

Lecture 131 Building Type 2 Dimensions - Using Data Flows - Step1

Lecture 132 Building Type 2 Dimensions - Using Data Flows - Step2

Lecture 133 Building Type 2 Dimensions - Pipeline Review

Lecture 134 Building Type 2 Dimensions - Using Stored Procedures

Lecture 135 Building Dimensions - Build remaining dimensions

Lecture 136 Facts - Overview

Lecture 137 Building Facts

Lecture 138 Data Warehouse Review and Data Analysis

Lecture 139 Module Summary

Section 12: Building the Delta Lake

Lecture 140 Module Overview

Lecture 141 Recap of what we implemented

Lecture 142 What we will implement

Lecture 143 Azure Databricks

Lecture 144 What is Azure Databricks

Lecture 145 Core Artifacts of Azure Databricks

Lecture 146 Setup Azure Databricks

Lecture 147 Setup Databricks Resource

Lecture 148 Databricks UI Overview

Lecture 149 Databricks Cluster Overview

Lecture 150 Create Databricks Cluster

Lecture 151 Azure Service Principal and Access to Data Lake Storage

Lecture 152 Mount Azure Data Lake Storage

Lecture 153 Overview of Delta Lake Implementation

Lecture 154 What is a Delta Lake

Lecture 155 Create Data Source for the Delta Table

Lecture 156 Create Delta Table

Lecture 157 Load Delta Table

Lecture 158 Update Delta Table

Lecture 159 Delta Table Concepts

Lecture 160 Create Linked Service to Databricks from Data Factory

Lecture 161 Executing Databricks Notebook from Data Factory

Lecture 162 Module Summary

Section 13: Presentation Layer

Lecture 163 Module Overview

Lecture 164 Overview - Modern Data Warehouse

Lecture 165 Overview - What we implemented

Lecture 166 Overview - What we will implement

Lecture 167 PowerBI - Installation

Lecture 168 PowerBI - Overview

Lecture 169 PowerBI - Connecting to the Data Warehouse

Lecture 170 PowerBI - Building the Tabular Model

Lecture 171 PowerBI - Building the Report

Lecture 172 PowerBI - Report Requirements

Lecture 173 PowerBI - Report Review

Lecture 174 Module Summary

Section 14: Triggers

Lecture 175 Module Overview

Lecture 176 Overview of Triggers

Lecture 177 Approach to Pipeline Execution

Lecture 178 Implementing a Master Pipeline

Lecture 179 Executing the Master Pipeline

Lecture 180 Implementing Event-based triggers

Lecture 181 Executing Event-based Triggers

Lecture 182 Scheduling Pipelines

Lecture 183 Creating a Tumbling Window Trigger

Lecture 184 Module Summary

Section 15: Monitoring

Lecture 185 Module Overview

Lecture 186 Executing Event-based triggers

Lecture 187 Overview of Data Factory Monitoring

Lecture 188 What do we monitor in Azure Data Factory

Lecture 189 Visual Monitoring in Azure Data Factory

Lecture 190 Pipeline Recovery

Lecture 191 Setup Alerts

Lecture 192 Validate the Alert

Lecture 193 Metrics

Lecture 194 Module Summary

Section 16: Section 16: Conclusion

Lecture 195 Summary

Beginners or Students who want to break into the Data Engineering field,Developers who want to learn Data Engineering,Data Engineers who want to learn how to implement a Modern Data Warehouse through a step-by-step approach,Data Engineers/Data Warehouse developers who want to get the skills necessary in implementing cloud based data engineering solutions,Data Engineers who want to understand how to build and end-to-end solution using Azure Data Factory (ADF)